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Kraft, Matthew A. – Annenberg Institute for School Reform at Brown University, 2019
Researchers commonly interpret effect sizes by applying benchmarks proposed by Cohen over a half century ago. However, effects that are small by Cohen's standards are large relative to the impacts of most field-based interventions. These benchmarks also fail to consider important differences in study features, program costs, and scalability. In…
Descriptors: Data Interpretation, Effect Size, Intervention, Benchmarking
Henry, Philip – College and University, 2019
Phillip Henry is a semi-retired former U.K. Registrar and Secretary with almost 40 years' experience in higher education. He has been active in staff development in the United Kingdom (Association of University Administrators, Academic Registrars Council, and Association of Heads of University Administration), in the United States (AACRAO and a…
Descriptors: Academic Achievement, Administrator Attitudes, Educational Experience, College Students
Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen – American Journal of Evaluation, 2016
Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…
Descriptors: Intervention, Multivariate Analysis, Mixed Methods Research, Models
Kotrlik, Joe W.; Williams, Heather A.; Jabor, M. Khata – Journal of Agricultural Education, 2011
The Journal of Agricultural Education (JAE) requires authors to follow the guidelines stated in the Publication Manual of the American Psychological Association [APA] (2009) in preparing research manuscripts, and to utilize accepted research and statistical methods in conducting quantitative research studies. The APA recommends the reporting of…
Descriptors: Agricultural Education, Statistical Significance, Effect Size, Educational Research
Bridgeman, Brent; Burton, Nancy; Cline, Frederick – Applied Measurement in Education, 2009
Descriptions of validity results based solely on correlation coefficients or percent of the variance accounted for are not merely difficult to interpret, they are likely to be misinterpreted. Predictors that apparently account for a small percent of the variance may actually be highly important from a practical perspective. This study combined two…
Descriptors: Predictive Validity, College Entrance Examinations, Graduate Study, Grade Point Average
Douzenis, Cordelia; Rakow, Ernest A. – 1987
Outliers, extreme data values relative to others in a sample, may distort statistics that assume internal levels of measurement and normal distribution. The outlier may be a valid value or an error. Several procedures are available for identifying outliers, and each may be applied to errors of prediction from the regression lines for utility in a…
Descriptors: Correlation, Data Analysis, Data Interpretation, Statistical Analysis

Marcoulides, George A.; Drezner, Zvi – Educational and Psychological Measurement, 1993
A procedure is presented to transform an n-dimensional scatter diagram into a two-dimensional scatter diagram while preserving proximity relationships between points. This procedure can help in the presentation and interpretation of multivariate results. (Author/SLD)
Descriptors: Correlation, Data Analysis, Data Interpretation, Multivariate Analysis
Stratton, Leslie S.; O'Toole, Dennis M.; Wetzel, James N. – Research in Higher Education, 2007
We use data from the 1990/1994 Beginning Post-Secondary Survey to determine whether the factors associated with long-term attrition from higher education differ for students who initially enrolled part-time as compared to for students who initially enrolled full-time. Using a two-stage sequential decision model to analyze the initial enrollment…
Descriptors: Student Characteristics, Enrollment Trends, Student Attrition, Dropout Research

ter Braak, Cajo J. F. – Psychometrika, 1990
Canonical weights and structure correlations are used to construct low dimensional views of the relationships between two sets of variables. These views, in the form of biplots, display familiar statistics: correlations between pairs of variables, and regression coefficients. (SLD)
Descriptors: Correlation, Data Interpretation, Equations (Mathematics), Factor Analysis

Zegers, Frits E. – Applied Psychological Measurement, 1991
The degree of agreement between two raters rating several objects for a single characteristic can be expressed through an association coefficient, such as the Pearson product-moment correlation. How to select an appropriate association coefficient, and the desirable properties and uses of a class of such coefficients--the Euclidean…
Descriptors: Classification, Correlation, Data Interpretation, Equations (Mathematics)
Landrum, Timothy; Katsiyannis, Antonis; Archwamety, Teara – Behavioral Disorders, 2004
The authors analyzed student data from the U.S. Department of Education's "Annual Reports to Congress on the Implementation of the Individuals with Disabilities Education Act" to asses patterns and trends in the educational placement of students with emotional or behavioral disorders (E/BD), as well as their means of school exit and the…
Descriptors: Behavior Disorders, Student Placement, Emotional Disturbances, Annual Reports